In digital communication systems a transmitted signal is normally encoded, modulated and transmitted over a communication channel. The signal is sent in form of pulses that can be detected either 0 and 1 and given combinations of these “bits” form symbols with different meanings. One or more signal sequences sent in a packet data stream is called a “burst”.
The information signal is sent by modulation of a carrier wave. Different modulation methods exist, of which the most usual are based on amplitude, frequency and/or phase modulation.
It is advantageous to describe different modulation forms by a complex signal representation. The complex signal represents the real physical bandpass signal in an equivalent lowpass representation. Essential features of the different modulation forms can then be represented geometrically. For example in one-dimensional modulation, the signal constellation points are located on a straight line.
Furthermore, there is a difference between linear modulation (different forms of amplitude modulation) and constant envelope modulation (angle-, phase and frequency modulation). However, almost all modulation methods used in mobile telephony can be interpreted as linear modulation with some modifications. In linear modulation, the modulator and the demodulator carry out some signal handling functions to send information by modulation of a carrier wave.
Whatever the physical medium used for transmission of the signal is, the essential feature is that the channel distorts the signal in different ways. The distorted signal is further corrupted by noise and interference. At the receiver, the signal is reconstructed and demodulated. The reconstructed signal is processed by channel and source decoders to obtain a copy of the transmitted source signal.
In a receiver with only one antenna the demodulator normally tries to combat distortion caused by the communication channel and noise but neglects the effects of interference. In order to derive efficient algorithms, the noise is normally considered to be white. If the noise is not white, a noise whitening filter can be used but this is not normally done since the color of the noise is unknown.
Filtering structures can be used to perform interference rejection. Adaptive or non-adaptive algorithms can be used to adjust the parameters in these filtering structures so that the desired signal can be estimated. After the filtering structures, demodulators demodulate the estimated signal of interest.
There are two classes of methods for choosing the parameter values. Conventional methods rely on the knowledge of training sequences embedded in the desired signals, this knowledge being used to choose an initial setting for the coefficient values, typically according to a least-squares error criterion. In an adaptation by means of a training sequence, the received data are used to find the parameters. Blind methods use one or more properties of the interference or of the desired signals instead of training sequences. Conventional methods that use a known training signal are well known to those skilled in the art of signal processing and are described in the open literature of adaptive signal processing and adaptive filtering.
The desired signal is estimated by a sequence estimator, of which there are several versions known in the art. These estimators make use of algorithms to mathematically calculate the desired signal by using filter parameters.
The most common sequence estimator used in receiver structures is the Maximum Likelihood Sequence Estimator (MLSE). The MLSE is preferably implemented with the so-called Viterbi algorithm. A version of this algorithm is the Soft Output Viterbi Algorithm (SOVA), which improves the performance of the subsequent channel decoding of the signal. The Maximum A Posteriori (MAP) algorithm gives superior performance, but is computationally more difficult. In the Decision Feedback Equalizer (DFE), already decided symbols in the process are used to decide the current symbol. A compromise between MLSE and DFE in performance is the Decision Feedback Sequence Estimator (DFSE), where it is possible to make a trade-off between computational complexity and performance. In the case with more than one antenna, some receivers can also consider the interference.
The main drawback with existing one-antenna solutions is that the algorithms consider only noise. Meanwhile, many communication systems are interference limited. The problem is that when the properties of the interference do not fit the model of the noise for which the receivers were designed, the performance of the receivers is greatly degraded.
The Interference Rejection Combining (IRC) receiver combines the output from antennas while trying to minimize the effect of noise and interference. There is an algorithm in the literature that considers the interference as a colored process. In deriving this algorithm, it is assumed that the noise can be described as a white sequence passed through an Auto Regressive (AR) filter.
The following patent documents are presented as prior art solutions in interference cancellation by means of different filter structures and for estimation of the desired signal by selection of suitable filter parameters for the filter structures.
In WO 98/16021 there is presented prior art solution for extracting a signal of interest from plurality of spectrally and temporally overlapping signals containing digital data. The apparatus of this solution comprises different filters producing time shifted output signals, frequency shifted output signals and linear combining means for summing said output signals and to produce an estimate of said signal of interest.
WO 97/11544 makes use of a noise-predictive maximum-likelihood (NPML) data detection scheme operating on signal samples received via an equalizing filter.
EP 0782260 A2 presents an equalizer configuration for processing real-valued and complex-valued signal samples.
The development of the mobile communication today is towards more and more users, which leads to increasing interference. This means that it is of great importance to find better and better methods for interference rejection and cancellation all the time and that every advantage within this area is useful.